door entry system alina dinca lászló papp adrian ulges csaba domokos cercel constantin team:
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What is Project 9 about?
Name: Door entry system – feature analysis of a face using point separation
Input: images of several faces Operation: Identify key points (eyes, end of nose, mouth).
Measure distances and angles between these (for different orientations). Feed the results into a statistical analysis routine. Identify for unknown image most likely match.
Coding: C++, Matlab Remarks: difficulty quite hard
algorithm for .pgm reader extract 64/64 keypoint cut-outs make an average (pattern) for each group
of cut-outs
Step 1 Locating key points
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transform the patterns .pgms with Fast Fourier Transformation transform the input image with Fast Fourier Transformation convolute the input image with each pattern to find the maximum transform them back from the Fourier space
Idea1. Using FFT => didn’t work!
FFT
FFT
*Response image
Inverse FFT
The formula for it is:
from {-1, 1}. If almost 1, then we have a match!! Get the maximum Slow algorithm (2½ minutes)
Idea 2. Similarity measure: correlation
maximumCorrelation image
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2nd scaling1st scaling
1) Scaling the input and the average twice
2) Match in small image3) Find the match and scale back the match
4) Faster algorithm (6 seconds)
2nd scaling1st scaling
Idea 2. => Hierarchical Matching
--- A faster aproach ---
Input
Average
64/64
Evaluation:
10 pictures from the data base search eyes, noses, lips visual inspection Results
eye - 80% nose - 80% lip - 20%
Side knowledge about
keypoints?
use 20 key points from Data Base feature vectors: normalized coordinates (form a neuronal network)
use the nearest neighbour
Evaluation: - 1020 data records
- 510 training set
- 510 test set
- results: 98% recognition rate
Step 2 Make the classification
Acces denied Acces granted
New image
Training
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